统计推断原理
统计推断原理
图书信息
出版社: 人民邮电出版社; 第1版 (2009年8月1日)
外文书名: Principles of Statistical Inference
丛书名: 图灵原版数字统计学系列
平装: 219页
正文语种: 英语
开本: 16
ISBN: 9787115210746
条形码: 9787115210746
尺寸: 23.4 x 16.6 x 1.2 cm
重量: 399 g
作者简介
作者：(英国) 考克斯 (Cox.D.R.)
D.R.Cox，世界著名统计学家，英国皇家学会会员暨英国社会科学院院士，美国科学院、丹麦皇家科学院外籍院士。曾任国际统计协会、伯努利数理统汁与概率学会、英国皇家统计学会主席。主要学术贡献包括Cox过程和影响深远且应用广泛的Cox比例风险模型等。
内容简介
《统计推断原理(英文版)》是统计学名家名作，包含9章内容和两个附录，前面几章介绍一些基本概念，如参数、似然、主元等，然后介绍显著性检验、渐进理论以及比较复杂的统计推断问题。还特别介绍了实验设计中基于随机化的统计推断。核心概念的解释非常清晰，即使跳过其中的数学细节，也能使读者理解。
《统计推断原理(英文版)》可作为工科、管理类学科专业本科生、研究生的教材或参考书，也可供教师、工程技术人员自学之用。
媒体评论
“这是伟大统计学家的伟大著作。千万不能错过！
——Ronaid Christensen。Journal of the American StatisticaI Association
“本书是现代统计学之父的力作，深入阐述了统计推断的内容，行文流畅、语言优美。对所有从事统计工作的人来说，本书不可不读。”
——Davtd Hand（伦敦大学帝国学院）
“非常优秀的一本教材，在频率学派和贝叶斯学派之间找到了绝好的平衡，给出不偏不倚的观点。”
——《应用统计》杂志
目录
1 Preliminaries
Summary
1.1 Starting point
1.2 Role of formal theory of inference
1.3 Some simple models
1.4 Formulation of objectives
1.5 Two broad approaches to statistical inference
1.6 Some further discussion
1.7 Parameters
Notes 1
2 Some concepts and simple applications
Summary
2.1 Likelihood
2.2 Sufficiency
2.3 Exponential family
2.4 Choice of priors for exponential family problems
2.5 Simple frequentist discussion
2.6 Pivots
Notes 2
3 Significance tests
Summary
3.1 General remarks
3.2 Simple significance test
3.3 One- and two-sided tests
3.4 Relation with acceptance and rejection
3.5 Formulation of alternatives and test statistics
3.6 Relation with interval estimation
3.7 Interpretation of significance tests
3.8 Bayesian testing
Notes 3
4 More complicated situations
Summary
4.1 General remarks
4.2 General Bayesian formulation
4.3 Frequentist analysis
4.4 Some more general frequentist developments
4.5 Some further Bayesian examples
Notes 4
5 Interpretations of uncertainty
Summary
5.1 General remarks
5.2 Broad roles of probability
5.3 Frequentist interpretation of upper limits
5.4 Neyman-Pearson operational criteria
5.5 Some general aspects of the frequentist approach
5.6 Yet more on the frequentist approach
5.7 Personalistic probability
5.8 Impersonal degree of belief
5.9 Reference priors
5.10 Temporal coherency
5.11 Degree of belief and frequency
5.12 Statistical implementation of Bayesian analysis
5.13 Model uncertainty
5.14 Consistency of data and prior
5.15 Relevance of frequentist assessment
5.16 Sequential stopping
5.17 A simple classification problem
Notes 5
6 Asymptotic theory
Summary
6.1 General remarks
6.2 Scalar parameter
6.3 Multidimensional parameter
6.4 Nuisance parameters
6.5 Tests and model reduction
6.6 Comparative discussion
6.7 Profile likelihood as an information summarizer
6.8 Constrained estimation
6.9 Semi-asymptotic arguments
6.10 Numerical-analytic aspects
6.11 Higher-order asymptotics
Notes 6
7 Further aspects of maximum likelihood
Summary
7.1 Multimodal likelihoods
7.2 Irregular form
7.3 Singular information matrix
7.4 Failure of model
7.5 Unusual parameter space
7.6 Modified likelihoods
Notes 7
8 Additional objectives
Summary
8.1 Prediction
8.2 Decision analysis
8.3 Point estimation
8.4 Non-likelihood-based methods
Notes 8
9 Randomization-based analysis
Summary
9.1 General remarks
9.2 Sampling a finite population
9.3 Design of experiments
Notes 9
Appendix A: A brief history
Appendix B: A personal view
References
Author index
Subject index